Upon mapping swarm robots search to particleswarmoptimisation (PSO) and proposing concept of lime-varying character swarm (TVCS), the authors extend PSO to model swarm robotic system. Based on control principle of e...
详细信息
Upon mapping swarm robots search to particleswarmoptimisation (PSO) and proposing concept of lime-varying character swarm (TVCS), the authors extend PSO to model swarm robotic system. Based on control principle of expected evolution position, an asynchronous communication policy is presented. Robot detects target signals in parallel to decide expected evolution position. The required time steps for completing the distance between two consecutive expected positions depend on kinematics constraints of robot. Meanwhile, robot evaluates positions it passes in every time step, updating its cognition as soon as when a better finding of itself has been found, updating shared information and broadcasting within TVCS if a better finding of swami appears. Either listening change of shared information or reaching the current expected position, robot starts to compute new expected position and turn out next control round. Simulation results indicate that the presented communication strategy has advantage over popular ones in search efficiency.
This paper presents investigations into modelling of a single-link flexible manipulator system using the particleswarmoptimisation (PSO) algorithm. PSO is a population-based search algorithm and is initialised with ...
详细信息
This paper presents investigations into modelling of a single-link flexible manipulator system using the particleswarmoptimisation (PSO) algorithm. PSO is a population-based search algorithm and is initialised with a population of random solutions, called particles. Basic PSO with best model can hardly provide suitable solutions in the case of real-world multimodal problems. In order to improve diversity in the population set and hence to improve the global searching capability a local version of PSO with time varying inertia and acceleration coefficients is proposed and used in this work. The effectiveness of the algorithm in modelling is validated and verified in terms of tracking, stability and the ability of the derived model in capturing a system's dynamics. The effect of swarm size on the convergence of the proposed PSO algorithm is also analysed. Time domain and frequency domain results of derived models clearly show the potential of the modelling technique and the proposed algorithm in solving such control problems.
暂无评论